Acoustic Steering via Multiaxial Driving: A Study on the Effect of Element Geometry
Bibliographic record
Abstract
This study investigates the impact of piezoelectric lateral length on acoustic wave steering in multiaxially driven piezoelectric transducers. Multiaxial driving employs two orthogonal electric fields applied through distinct electrode sets to enable directional control of the acoustic field. To explore the relationship between transducer geometry and steerability, a series of simulations were conducted using PZT elements with fixed thickness and varying lateral dimensions. Resonance frequencies in both the propagation and lateral directions were analyzed as a function of the lateral-to-thickness (L/P) ratio. Three representative transducer designs were selected based on the proximity of their resonance frequencies in the two driving directions and were subsequently fabricated and measured. Acoustic profiles were measured with varying phase offsets between the driving signals. The results show that effective acoustic steering occurs when the resonance frequencies of the propagation and lateral modes are closely matched. As the frequency difference increases, the steering range diminishes, eventually leading to no observable steering. Additionally, resonance frequency trends indicate that increasing lateral length lowers both propagation and lateral-mode frequencies. Experimental measurements validated the simulation trends. The L/P ratio proves to be a useful parameter for characterizing resonance behavior and guiding transducer design. This work provides a foundation for optimizing piezoelectric geometries to enhance acoustic wave steering in single-element actuators, with implications for improving the performance of multiaxial ultrasound applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".